Pharmacokinetic State Space Models for Unbiased Prediction of Haemodynamic Collapse
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An Intraoperative Hypotension (IOH) event is a frequent complication during administration of general anaesthesia with serious downstream consequences, yet clinical management remains reactive and not...
The study introduces SynerT, a waveform-only hybrid temporal model that uses a causal dilated TCN and dilated recurrent layers to predict early intraoperative acute kidney injury (AKI). Two extensions, SynerT-MM and SynerT-Stack, incorporate hemodynamic summaries, preoperative covariates, and a leakage-safe stacked ensemble to improve discrimination and calibration. Evaluated on the VitalDB database with a strict 60‑minute prediction window, SynerT-Stack achieved the best performance across AUROC, AUPRC, and F1‑max, and demonstrated the greatest net clinical benefit after recalibration.
arXiv:2608. 08920v1 Announce Type: new Abstract: Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability.
This study develops a rolling day‑wise mortality prediction model for critically ill patients with acute kidney injury on continuous renal replacement therapy (CRRT) by incorporating minute‑level machine pressure waveforms alongside electronic health record (EHR) data. After cleaning the raw machine data—aligning it with therapy intervals, removing priming and downtime minutes, and denoising artifacts—the authors trained a transformer‑based stacked ensemble that fuses a sequence transformer with classical models using circuit‑instability features and clinical variables. In a multi‑center benchmark of 976 patients, the combined model achieved a one‑day mortality AUROC of 0.766, outperforming machine‑only (0.625) and EHR‑only (0.717) models, and SHAP analysis highlighted key pressure‑related features such as filter pressure, transmembrane pressure, and access‑to‑return difference. whyItMatters":"By integrating previously discarded CRRT machine data, the study provides a continuous, real‑time risk signal that could enable earlier detection of patient deterioration and improve mortality prediction in a high‑risk population."
The paper introduces a bounded updating framework for ICU prediction models that freezes the physiological encoder while allowing updates only to the treatment pathway and fusion head. Experiments on 84,792 MIMIC-IV ICU stays across four temporal shifts show that selective adaptation yields more stable explanations—higher rank correlation, better top‑5 feature agreement, and improved retrieval stability—compared to full model adaptation. Predictive performance varies by outcome, but the results demonstrate that explanation stability is governed by the structural boundaries of allowed adaptation rather than merely by freezing components.
arXiv:2609.22959v1 Announce Type: cross Abstract: The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether m...